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Bayesian methods for multi-objective optimization of a supersonic wing planform
Jim T.M.S.a, Faza G.A.b, Palar P.S.b, Shimoyama K.a
a IFS and Dept. of Aerospace Eng., Tohoku University Sendai, Miyagi, 980-8577, Japan
b Institut Teknologi Bandung, Bandung, West Java, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 ACM.The global design1 of a supersonic wing planform is demonstrated using gradient-based and gradient-free surrogate-assisted Bayesian optimization utilizing expected hypervolume improvement as the optimization metric. The planform is parameterized using 6- and 11-variables. Representative of a simple supersonic business-jet conceptual-level design, the wing-body is optimized for low inviscid drag and low A-weighted ground-level noise. The speed of convergence to the non-dominated front and suitability of the two optimization implementations are compared, and the advantages over using a genetic algorithm directly are observed. A novel method for initial candidate sample generation, effective non-dominated from sampling, is proposed to further accelerate the convergence of samples towards Pareto solutions.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Bayesian methods,Bayesian optimization,Conceptual levels,Pareto solution,Sample generations,Speed of convergence,Supersonic business jets,Supersonic wings[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Bayesian optimization,SST,Supersonic,Surrogate modelling[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1145/3377929.3398122[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]